Counts of cases, hospital admissions, and deaths are key metrics of COVID-19 prevalence and burden, and are the basis for model-based estimates and predictions of these statistics. I present here graphs showing these metrics over time in Washington state and a few other USA locations of interest to me. I update the graphs and this write-up weekly. Previous versions are here. See below for caveats and technical details.

The first several figures (1a-2d) show case and death counts per million for several Washington and non-Washington locations using data from Johns Hopkins Center for Systems Science and Engineering (JHU), described below. The Washington locations are the entire state, the Seattle area (King County) where I live, and the adjacent counties to the north and south (Snohomish and Pierce, resp.). The non-Washington locations are Ann Arbor, Boston, San Diego, and Washington DC.

Figures 1a-b (the top row) show smoothed case counts (See below for details on the smoothing method). Figures 1c-d (the bottom row) overlay raw data onto the smoothed since June 1 to help explain recent trends.

When comparing the smoothed Washington and non-Washington graphs (Figures 1a-b), please note the difference in y-scale; the graphs with raw data (Figures 1c-d) have the same y-scale and may be better for comparing Washington and non-Washington locations. The current raw counts for Washington locations are about 1750-3000 per million; non-Washington raw counts are about 1350-2650 per million.

The smoothed graphs for Washington locations (Figure 1a) show rates increasing statewide and in Snohomish (north of Seattle) but flat in Seattle and Pierce (south of Seattle). The raw data (Figure 1c) and trend analysis suggest that rates may be flattening in all locations. Rates in all Washington locations except Seattle have soared beyond all previous peaks; the Seattle rate remains slightly below its winter peak.

The smoothed graphs for non-Washington locations (Figure 1b) show rates increasing in all locations except San Diego. The raw data (Figure 1d) and trend analysis agree that the decline in San Diego is real; the recent data in other locations has become too variable to be confident in the direction, but looking back 6 or 8 weeks, the data concur that rates are increasing. Rates in non-Washington locations, though higher than a few months ago, remain below their winter peaks.

Figures 2a-d show deaths per million for the same locations. As with the cases graphs, the top row (Figures 2a-b) show smoothed data (see details below) and the bottom row (Figures 2c-d) overlays raw data onto the smoothed since June 1.

As with the cases data, the graphs with raw data (Figures 2c-d) are probably more useful when comparing the Washington and non-Washington graphs, as they have the same y-scale. The current Washington rates are 13-37 per million and non-Washington rates are 0-19 per million.

The smoothed graphs for Washington locations (Figure 2a) show rates increasing everywhere. Trend analysis and raw data (Figure 2c) confirm that rates are heading up statewide. Recent data for the individual locations are too variable to be confident in the direction, although looking back 6 or 8 weeks, the upward trend is clear. The long flat line for Pierce in late May-June 2021 is due to negative counts for two weeks in early June: this arises when JHU discovers they overcounted previous weeks and are catching up; my software clamps the fit to zero reasoning that negative counts are meaningless.

The smoothed graphs for non-Washington locations (Figure 2b) show rates going up in San Diego, while remaining flat or declining slightly everywhere else. Trend analysis and raw data (Figure 2d) concur with the caveat that San Diego data is becoming more variable.

The next graphs show the Washington results broken down by age. This data is from Washington State Department of Health (DOH) weekly downloads, described below. An important caveat is that the DOH download systematically undercounts events in recent weeks due to manual curation. I extrapolate data for late time points as discussed below.

In most versions between March 17 and September 1, 2021, I showed graphs for the entire state and not individual locations, since all locations were similar. In the current wave, geographic differences are substantial with Seattle (King County) doing much better than the rest of the state.

Figures 3a-d are cases.

Early on, the pandemic struck older age groups most heavily, but cases quickly spread into all age groups, even the young. Rates have greatly increased in recent weeks but are now flattening or declining in most age groups. Young adults (20-34 years) have been highest since summer 2020, with the next older group (35-49 years) close behind. As of this week, the youngest group (0-19) have caught up to the 35-49 year olds to take the dubious honor of being second highest. Pre-seniors (50-64) are well below the younger groups followed by the two oldest age groups (65-79 and 80+ years). The difference between worst and best in the statewide data (Figure 3a) is almost 2x (about 3800 for 20-34 vs. 2000 for 65-79). The general pattern is similar in Seattle (Figure 3b) but the counts are a lot lower, ranging from 1.8x lower for 0-19 (3600 vs. 1950) to 2.3x lower in the 80+ group (2150 vs. 950). The Snohomish numbers (Figure 3c) are between Seattle and statewide, while Pierce (Figure 3d) is about the same as the state.

Figures 4a-d show hospital admissions (admits) and deaths.

Admits, like cases, have gone up a lot in recent weeks and statewide are the highest they’ve ever been except for the oldest age group (80+). Admit rates vary with age groups exactly as one would expect: lowest in the young and increasing with age. As with cases, admits are much higher statewide than in Seattle (Figure 4a vs. 4b): more than 2x in the all groups except the youngest. The Snohomish rates (Figure 4c) are generally between Seattle and statewide, while Pierce (Figure 4d) is about the same as the state.

Throughout the pandemic, the death rate for the 80+ group was much higher than any other group. Thankfully deaths even in this group dropped to near zero in early summer but sadly have increased above their spring 2021 levels in most locations. Deaths for the oldest groups are higher statewide (Figure 4a) than any of the individual locations (Figures 4b-d). The high statewide rate reflects the much higher death rate in the rural eastern part of the state (data not shown).

The dip at the end is probably an artifact of DOH undercounting. This seems clear when comparing the figures here to those in the previous version, suggesting my extrapolation is undershooting deaths.

Caveats

  1. The term case means a person with a detected COVID infection. In some data sources, this includes “confirmed cases”, meaning people with positive molecular COVID tests, as well as “probable cases”. I believe JHU only includes confirmed cases based on the name of the file I download. In past, DOH only reported confirmed cases but as of August 29, 2021 they seems to be including probable ones, too. This doesn’t seem to have affected the totals much.

  2. Detected cases undercount actual cases by an unknown amount. When testing volume is higher, it’s reasonable to expect the detected count to get closer to the actual count. Modelers attempt to correct for this. I don’t include any such corrections here.

  3. The same issues apply to deaths to a lesser extent, except perhaps early in the pandemic.

  4. The geographic granularity in the underlying data is state or county. I refer to locations by city names reasoning that readers are more likely to know “Seattle” or “Ann Arbor” than “King” or “Washtenaw”.

  5. The date granularity in the graphs is weekly. The underlying JHU data is daily; I sum data by week before graphing.

  6. I truncate data to the last full week prior to the week reported here.

Technical Details

  1. I smooth the graphs using a smoothing spline (R’s smooth.spline) for visual appeal. This is especially important for the deaths graphs where the counts are so low that unsmoothed week-to-week variation makes the graphs hard to read.

  2. The Washington DOH data (used in Figures 3 and 4 to show counts broken down by age) systematically undercounts events in recent weeks due to manual curation. I attempt to correct this undercount through a linear extrapolation function (using R’s lm). I have tweaked the extrapolation repeatedly, even turning it off for a few weeks. The current version uses a model that combines date and recentness effects. In past, I created models for each Washington location and age group but had to change when DOH changed its age groups on March 14, 2021 (see below). I now create a single model for the state as a whole and all age groups summed together, then blithely apply that model to all locations and ages.

  3. The trend analysis computes a linear regression (using R’s lm) over the most recent four, six, or eight weeks of data and reports the computed slope and the p-value for the slope. I also compute a regression using daily data over the most recent 7-42 days. In essence, this compares the trend to the null hypothesis that the true counts are constant and the observed points are randomly selected from a normal distribution. After looking at trend results across the entire time series, I determined that p-values below 0.15 indicate convincing trends; this cutoff is arbitrary, of course.

Data Sources

Washington State Department of Health (DOH)

DOH provides three COVID data streams.

  1. Washington Disease Reporting System (WDRS) provides daily “hot off the presses” results for use by public health officials, health care providers, and qualified researchers. It is not available to the general public, including yours truly.

  2. COVID-19 Data Dashboard provides a web graphical user interface to summary data from WDRS for the general public. (At least, I think the data is from WDRS - they don’t actually say).

  3. Weekly data downloads (available from the Data Dashboard web page) of data curated by DOH staff. The curation corrects errors in the daily feed, such as, duplicate reports, multiple test results for the same incident (e.g., initial and confirmation tests for the same individual), incorrect reporting dates, incorrect county assignments (e.g., when an individual crosses county lines to get tested).

The weekly DOH download reports data by age group. In past, the groups were 20-year ranges starting with 0-19, with a final group for 80+. As of the March 14, 2021 data release (corresponding to document version March 17), they changed the groups to 0-19, followed by several 15 year ranges (20-34, 35-49, 50-64, 65-79), with a final group for 80+. They changed age groups again in the August 29, 2021 data release (corresponding to document version September 1), but I chose to map the new age groups to the previous ones to avoid software difficulties. The new groups are 4-10, 11-13, 14-19, 0-11, 12-19, 20-34, 35-49, 50-64, 65-79, 80+; I reconstitute 0-19 by summing 0-11 and 12-19 and remove the other young groups.

Issues with DOH Undercounting

Figures 5a-b compare DOH and JHU cases and deaths for Washington state to illustrate the undercount in the raw DOH data. The cases data matches well except for a few periods spanning several weeks, including most of May 2020. The deaths data matches less well and is presently much lower than JHU. I believe the discrepancy in the deaths date reflects the consistent undercount of recent DOH data.

Johns Hopkins Center for Systems Science and Engineering (JHU)

JHU CSSE has created an impressive portal for COVID data and analysis. They provide their data to the public through a GitHub repository. The data I use is from the csse_covid_19_data/csse_covid_19_time_series directory: time_series_covid19_confirmed_US.csv for cases and time_series_covid19_deaths_US.csv for deaths.

JHU updates the data daily. I download the data the same day as the DOH data (now Tuesdays) for operational convenience.

Other Data Sources

The population data used for the per capita calculations is from Census Reporter. The file connecting Census Reporter geoids to counties is the Census Bureau Gazetteer.

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